Skip to main content

Thank you for visiting You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Fraud in highly appreciated fish detected from DNA in Europe may undermine the Development Goal of sustainable fishing in Africa


Despite high effort for food traceability to ensure safe and sustainable consumption, mislabeling persists on seafood markets. Determining what drives deliberate fraud is necessary to improve food authenticity and sustainability. In this study, the relationship between consumer’s appreciation and fraudulent mislabeling was assessed through a combination of a survey on consumer’s preferences (N = 1608) and molecular tools applied to fish samples commercialized by European companies. We analyzed 401 samples of fish highly consumed in Europe and worldwide (i.e. tuna, hake, anchovy, and blue whiting) through PCR-amplification and sequencing of a suite of DNA markers. Results revealed low mislabeling rate (1.9%), with a higher mislabeling risk in non-recognizable products and significant mediation of fish price between consumer´s appreciation and mislabeling risk of a species. Furthermore, the use of endangered species (e.g. Thunnus thynnus), tuna juveniles for anchovy, and still not regulated Merluccius polli hake as substitutes, points towards illegal, unreported and/or unregulated fishing from African waters. These findings reveal a worrying intentional fraud that hampers the goal of sustainable seafood production and consumption, and suggest to prioritize control efforts on highly appreciated species.


For the accomplishment of Goal 12 “Responsible Production and Consumption” (UN Sustainable Development Goals, SDG, accessed in January 2021), one of the priorities in food sciences today is the traceability of the food products to be able to authenticate the species and the geographic origin. It is especially important to obtain food directly from natural populations, by hunting, fishing or harvesting. Only knowing their real exploitation level can the natural populations be sustainably managed. When a species is sold for another, the substitute is exploited without reporting. Moreover, if the substitute species is cheaper, the consumer will pay an unfair higher price for the product they believe is consumed.

Despite the obvious interest of honest labeling to warrant both the respect of consumers’ rights and resource sustainability, and increased control efforts, fish mislabeling is still happening in all continents involving species from all the seas (e.g.1,2,3,4,5. In some marine species as the Atlantic cod Gadus morhua mislabeling is decreasing6 while in others like hake it has oscillated over the last decades and seems to be increasing in the last years7. DNA analysis is widely used for becoming increasingly affordable, even when multiple markers are needed1,8,9. Not always mislabeling is synonymous of fraud. Sometimes it is accidental, for example when different species are caught together in mixed fisheries and cannot be distinguished easily from morphology, as megrims of the genus Lepidorhombus or several hakes of the genus Merluccius10. In other cases, however, mislabeling is deliberate so it can be considered fraudulent. In contrast with accidental mislabeling, deliberate mislabeling is more frequent in unrecognizable (versus recognizable) products11,12. In recognizable products the head is present and the whole body contains morphological characteristics that serve to identify the species, while the species cannot be ascertained visually in unrecognizable products. Often substitute species are cheaper than the original one13,14; fraudulent seafood mislabeling of economic cause happens against the interest of the consumer. Another type of fraudulent mislabeling occurs as a consequence of illegal, unreported and/or unregulated fishing (IUU); in those cases the defrauder benefits from selling inadvertently species that are often poorly managed and wild caught15. Examples are the covered exploitation of protected species, (e.g., high levels of mislabeling concealing endangered species have reported in sharks16,17, or selling individuals caught over quota as suggested in Miller et al.18. This is especially pernicious not only for species conservation but also for the society, because selling IUU catch undermines consumer’s awareness and conscious efforts of sustainable consumption19.

In the control of deliberate mislabeling, a priority is to determine the point of the commercial chain and the type of product where it happens more frequently20; that is to know mislabeling niches for control to be reinforced therein21. Is there a role of consumer’s appreciation in fish mislabeling? Apparently, it is especially frequent in highly appreciated products, like sushi22, processed cod products23, scarce and expensive Bluefin tuna Thunnus thynnus that is commonly replaced by other tuna species24,25. In other words, defrauders sell what the consumer wants whether the product is real or not. However, fraudulent mislabeling having an important economic component, defrauders will substitute preferentially, if not only, expensive species.

In this study we will focus on the relationship between consumer’s appreciation and fraudulent mislabeling in Spain. This country was chosen for having been identified as one of the countries with highest seafood consumption (42.4 kg/person/year) and also seafood mislabeling in the world14. We considered two species pairs of blue and white fish, one cheap and one expensive from each type. We analyzed consumer’s appreciation from self-reported consumption habits, and, using a suite of DNA markers to authenticate seafood samples, we determined mislabeling in recognizable and unrecognizable fish products.

Expectations were: First, in deliberate mislabeling a higher proportion in not recognizable versus recognizable products and expensive versus cheap species (Hypothesis a). Second, and central to this study, price will mediate between consumer’s appreciation and % of substitution, more expensive species being more mislabeled regardless consumer’s appreciation (Hypothesis b).


Fish mislabeling

In total we analyzed genetically 401 fish samples commercialized by European companies, obtained from 11 stores. DNA extraction and PCR amplification were successful in 365 samples (91% of success): 83 labeled as anchovy (72 with the scientific name Engraulis encrasicolus, 11 Engraulis sp.), 96 as hake (40 Merluccius merluccius, 29 M. hubbsi, 27 M. capensis/M. paradoxus), 96 as tuna (57 Thunnus alalunga, 36 T. albacares, 3 T. obesus) and 90 as blue whiting (Micromessistius poutassou). The sequences of the genetic barcodes were aligned within species, and the haplotypes (sequence variants) submitted to GenBank, where they are available with the accession numbers MW557502-MW557516 for control region barcodes and MW549508-MW549521 for COI barcodes.

Mislabeling was found in seven samples, 1.9% of the total samples analyzed (Table 1): two of anchovy (one product per sample), two of hake (four products, one in one sample and three in other) and three of tuna (five products in total: one, two and two per sample). The total proportion of mislabeling was 0% in blue whiting, 2.4% in anchovy, 4.17% in hake and 5.2% in tuna. From the label, five of the 11 mislabeled products were caught in front of African coasts (Center-NE Atlantic, Central E Atlantic, SE Atlantic), four from European waters (NE Atlantic, Aegean Sea) and two had no geographic information in the label (Table 2).

Table 1 Mislabeling found in this study.
Table 2 Mislabeled and substitute species.

Mislabeling was significantly more frequent in not recognizable products (10 out of 11 mislabeled products, Table 2), and in expensive species that are tuna and hake in this region (only two anchovies and none blue whiting). Mislabeling risk ratio RR of non recognizable versus recognizable products was 1.0369, 95% confidence [1.005–1.07], being significantly higher than 1 with z = 2.292 and p = 0.022. Similarly, mislabeling RR of expensive versus cheap species was 1.0371, 95% confidence [1.001–1.074], also significant (z = 2.022 with p = 0.043).

A deeper view at the mislabeled products (Table 2) suggests that the mislabeling found in this study could be compatible with IUU, at least in some cases. Mislabeled anchovies were about 12–13 cm long, and substitute species were small tuna fry or juveniles, one identified from DNA as yellowfin Thunnus albacares (the capture region stated on the label was NE Atlantic), and the other as Thunnus alalunga (captured from the Aegean Sea in the Mediterranean basin). The first one was sold whole, in fresh, eviscerated but with head and tail thus it was recognizable; the other was frozen headless, tailless and eviscerated. Under European (Regulation EU 850/98 of the European Council of 30 March, 1998) and Spanish laws (Royal Decree 560/1995 of 7 April, 1995), fish juveniles are protected and these tuna juveniles cannot be caught nor commercialized, thus the origin of these substitute fish was unequivocally IUU (Table 2). Small tuna fry and adult anchovies are indeed morphologically different but could be confounded by non-expert consumers, especially when sold headless. Engraulis encrasicolus distribution overlaps with spawning areas of Thunnus albacares in northeast African waters, and with those of Thunnus alalunga in the Mediterranean Sea, thus these substitutions are compatible with capturing tuna juveniles while fishing anchovy.

All the mislabeled hakes were sold frozen, in fillets or slices, labeled as Merluccius capensis/M. paradoxus that are South African hakes (Table 2). Three of them were substituted by M. polli that is an African species living from tropical and subtropical East Atlantic waters, and overlaps with M. capensis in the southern part of its distribution. South African M. capensis/paradoxus stocks are fished in a sustainable way certified by the Blue label of Marine Stewardship Council (, accessed January 2021), and they should not be caught together with other species. Thus the cause of this particular substitution is compatible with IUU (without discarding substitution in a later step). The other mislabeled hake was substituted by north Atlantic cod Gadus morhua which distribution does not overlap with south African hakes, thus substitution during fishing or landings was not possible in this case and must have occurred later in the commercial chain (Table 2).

Mislabeled tuna products were all labeled as “bonito del Norte” Thunnus alalunga (longfin or albacore tuna), which is the most frequently sold tuna species in the region. The three products that were substituted by northern Bluefin tuna Thunnus thynnus (Table 2) were all unrecognizable (slices), but the fish body (without the head) was visible and slices were cut fresh in front of the customer. They were sold in two different stores. The other two mislabeled samples did not report the capture region on the label. They were frozen slices, and were identified from DNA as yellowfin tuna Thunnus albacares and bigeye tuna Thunnus obesus, the last one with lower score and identity % with the best match in BLAST (Table 2). In the International Union for Conservation of Nature IUCN red list of threatened species26 Thunnus thynnus appears as globally endangered, T. obesus is vulnerable, and T. alalunga and T. albacares are near threatened. All the species overlap in the north—center east Atlantic and the EU has strict fishing quotas T. thynnus within a multiannual recovery plan (Regulation (EU) 2016/1627 of the European Parliament and of the Council of 14 September 2016).

Fish consumption and its relation with mislabeling

In the social survey (N = 1608 participants aged between 14 and 63 years) the average frequency of fish consumption was 3.13 (SD 0.923), that is, surveyed participants declared to eat any fish with a frequency between monthly and weekly (1 being the highest and 5 the lowest frequency, 3 being weekly), that is, at least monthly. The raw dataset can be found in Rodriguez et al.27.

A clear difference between species was found regarding consumption levels (Fig. 1). Tuna and hake were consumed weekly and monthly by a much larger proportion of participants than anchovy and whiting. Moreover, 49% and 61.6% of the participants declared not eating anchovy and blue whiting, respectively, being this proportion 20.7% for hake and 14.3% for tuna. The difference between species for the pattern of consumption frequency was highly significant (\(\chi_{4,4}^{2}\) = 1154.8 with p <  < 0.001).

Figure 1
figure 1

Consumption of the four species. Presented as the proportion of participants in the survey consuming a species weekly, monthly, occasionally or rarely/never.

Consumer’s appreciation of a species was estimated from the proportion of participants consuming it at a frequency at least equal to the average frequency of fish consumption, this meaning that the species accounts for an important proportion of the fish consumed. Since the average fish consumption frequency in the region was 3.13 (= between monthly and weekly), we added the classes “Weekly” and “Monthly” (i.e. at least monthly) consumption of a species as a proxy of its appreciation. Mislabeling was significantly correlated with both price (Fig. 2 above) and the consumer’s appreciation proxy (Fig. 2 below): r2 = 0.945 and 0.91 respectively, both with p < 0.05.

Figure 2
figure 2

Plots of mislabeling on fish price (above), and on consumer’s appreciation (below). Results of consumer appreciation are presented as % of weekly and monthly consumers of a species. Linear trends with their r2 value for slope different from 0 are presented. Color codes: Anchovy = green; hake = blue; tuna = orange; whiting = yellow.

The relationships between these variables are summarized in Fig. 3, with price as a mediator between consumer’s appreciation and fraudulent mislabeling. Zero-order unstandardized regression for predicting price from consumer’s appreciation (measured from consumption here) had B = 5.945 (SE = 0.806). Partial unstandardized regression coefficient for predicting mislabeling from fish price holding consumer’s appreciation constant was B = 0.015 with SE = 0.02 (Table 3). The distribution of products statistics Zα*Zβ was statistically significant being 5.376 > 2.18, p < 0.05. Mediation effect of price between consumer appreciation and mislabeling was thus significant in spite of limited number of species analyzed.

Figure 3
figure 3

Model of price as a mediator between consumer’s appreciation price and mislabeling. Arrows represent relationships between variables. Pearson’s r coefficients and their p-values are given (* for p < 0.05).

Table 3 Regression analysis of the variables mislabeling (dependent), price (expected mediator) and consumer’s appreciation (independent).


Combining a suite of molecular tools (three DNA markers and four different protocols) and a social survey amongst consumers, this work showed several evidences of fraud in appreciated species that are important for both food sciences and conservation. The mislabeling found in this study was likely fraudulent, since, according to hypothesis a, its risk was significantly higher in expensive than in cheap species, and in not recognizable than in recognizable products. Moreover, according to Hypothesis b it affected principally the species most appreciated by the consumer and amongst them those with higher prices, since price appeared to be a mediator between consumer’s appreciation (or, directly, consumption of a species) and mislabeling. This result points at the need of focusing on highly consumed highly priced species as priority mislabeling niche. Inadvertent mislabeling due to simple morphological confusion between species, frequently reported in previous studies in Spain10, does not seem the main cause of mislabeling here.

As in other studies, mislabeling occurred in tuna and hake. The level found was comparable to that published by other authors in Spain and elsewhere. Sotelo et al.25 found 6.7% mislabeling in fresh and frozen tuna in European markets of six countries between 2013 and 2014, similar to the 5.2% found here, although in Spain the level they found (20% in 20 samples) was clearly higher than in our study in which sampling was done six or five years later. Gordoa et al.24 also found higher levels of tuna mislabeling in Spanish supermarkets in samples of 2014–2015. It is possible that mislabeling is decreasing in Spain in the last years, following the current trend of improvement of market operations pointed out by Mariani et al.6 at European level. Thunnus albacares and T. obesus were the main substitutes in Sotelo et al.25 study, while T. thynnus was the most frequent substitute here. This reverse substitution (expensive T. thynnus for a cheaper species) has been rare, and when happening it was interpreted as indicative of illegal origin24. All together our results would indicate a change in tuna mislabeling trends in Spain.

Decrease of hake mislabeling seems to be happening in Europe as well, and the increase of the last years found in Blanco-Fernandez et al.7 review would be going down in Spain, since the proportion of mislabeled hake products found here (less than 5%) was low again. A difference with past mislabeling reports was the main substitute species found here, the Benguela hake Merluccius polli, rarely found as a substitute in other studies7. This would imply a change in hake substitutes and the unreported introduction of this species in European markets.

Anchovy mislabeling in Europe has been reported before28. It affects especially semipreserved products where it can as high as 16% being Engraulis anchoita the substitute species29. To our knowledge this is the first time that Thunnus is reported as a substitute of anchovy products. As in hake and tuna, it seems that the mislabeling trends are changing in this country towards different substitute species.

Although from our data and other studies6 fish mislabeling seems to be decreasing in Spain, the substitute species of this study are of concern for conservation because they point at the involvement of IUU, one of the causes of fraud identified by Fox et al.20, in current mislabeling in Spain. Finding small juveniles of near-threatened Thunnus albacares and T. alalunga sold as anchovy may reveal a serious problem in anchovy fisheries. If they catch juveniles of other species as by-catch, they are not sufficiently selective of the target species. Being true that the proportion was small, about 2.4% of the individuals analyzed, multiplying it by the tons of anchovy catch of Asturias fleet (1 566 059.4 kg in 2019 from official records; it would represent about 37,585 kg of unreported tuna fry, more than 35 tons. On the other hand, the use of endangered Thunnus thynnus (and perhaps the vulnerable Thunnus obesus too) in substitution of less threatened tuna species is, definitively, a call of attention for the increase of control in European tuna markets, as demanded by other researchers25. Since the catch areas of label and substitute species overlap, these substitutions would suggest that Bluefin tuna caught over quota is being sold as another species.

Finally, Merluccius polli –main substitute of M. capensis/paradoxus in hake products here- is now the subject of sustainable management plan designs in its northern distribution30, while in the center and south the stocks are largely unknown32. Introducing the species as a substitute in European markets impedes traceability of its catch. It could be introduced at industrial level (for example to complete a batch of slices or fillets), or be another example of the use of unregulated fishing products. Selling M. polli from IUU would hamper the efforts of sustainable management of African hakes, both the currently MSC-certified South African Merluccius capensis/paradoxus and the future of Benguela hake stocks. introduction of the species as a substitute dustrial.

In conclusion, these results reaffirm a general trend of decreasing mislabeling rates, which is to be expected as efforts in food sciences for better traceability continue. However, while inadvertent substitutions decrease, special attention must be directed towards the detection of deliberate fraud. The found substitutes are an indication for IUUs as a source for fraud. This is a cause for concern and calls for a focus on products that are especially susceptible to these actions; i.e. highly consumed, expensive products. Monitoring of this type of products must be reinforced in order to prevent that fraudulent practices reach the consumer’s plate. Moreover, the whole SDG 12 is undermined if conscious consumers cannot be sure of eating really their choice of sustainably caught fish.


Ethics statement

This study adheres to good research practices based on the European code of conduct for research integrity31. Since part of the research required the application of a population survey, the Committee of Research Ethics of Asturias Principality approved this study with reference 166/19. All methods were performed in accordance with relevant guidelines and regulations. Prior to the application of the survey, all participants were provided with an information sheet detailing the objectives of the research and that no personal data, except age and place of application, would be collected. Informed consent was obtained from all the participants. Likewise, their willingness to participate in the research was guaranteed. They were invited to verify their answers, to have access to the study results, and were informed about their right to withdraw from the process at any time.

Fish sampling

Fresh or frozen commercial products labeled as European anchovy (Engraulis encrasicolus), hake (genus Merluccius), tuna (genus Thunnus) and blue whiting (Micromessistius poutassou) were purchased from supermarkets, fishmongers and small shops in the region of Asturias (central south Bay of Biscay, Spain). Thus, they were products sold to consumers (final points in the commercial chain), all commercialized by European companies. Information about catch and prices of the four fish species in Asturias can be found in the General Directorate of Fisheries of Asturias Principality, (accessed in January 2021). Official statistics of fish prices in the region in 2019, when the samples were obtained, gave annual prices of 3.91, 3.89, 2 and 0.98 euro/kg of catch for tuna, hake, anchovy and blue whiting, respectively. Information about life cycles, distribution and spawning areas of these fish, and pictures, are available in FishBase website32 at

Sampling was designed randomly from the list of stores selling fish in the region. It took place in 2019–2020. Fish products and labels were photographed and the information annotated in writing for double recording. Regulations (EU) No 1379/2013 of the European Parliament and of the Council of 11 December 2013 on the common organization of the markets in fishery and aquaculture products, and (EU) No 1169/2011 of the European Parliament and of the Council of 25 October 2011 on the provision of food information to consumers apply in Spain. From these regulations, seafood labels must contain at least the following information: common and scientific names of the species; geographic origin (country of production, or FAO area if from marine fishing); production method (extractive fisheries or aquaculture).

Products selected were big enough or contained the tail or head, to be sure that each sample corresponded to one individual. When the product was packed (e.g. fillets or slices) we sampled only one piece by pack. Products were classed as recognizable (i.e. the head is present and the whole body contains morphological characteristics that serve to identify the species) or unrecognizable (the species cannot be ascertained visually). Muscle fragments from each sample were cut, preserved in 100% ethanol and stored in the laboratory at 4 °C until analysis.

Molecular analysis

DNA was extracted with Chelex-100 Resine (10%) and 4.5μL proteinase K (20 U/ml, by EURx) from frozen tissue. Different tools were employed for identification of the different fishes (Table 4). Cytochrome Oxidase Subunit I gene (COI) was employed for the first identification of blue whiting, anchovy and hake samples using primers described by Ward et al.33. Tuna was identified using the primer combination of L15998 with H-strand primer CSBDH, described by Alvarado-Bremer34 and used by Viñas and Tudela35 to distinguish between the eight Thunnus species from any kind of processed tissue, that amplify a fragment of the mitochondrial control region.

Table 4 Molecular tools employed in the analysis of fish fraud in this study, per species.

In some cases, a second marker was necessary to distinguish between different closely related species belonging to the same genus. For anchovy, mitochondrial control region was amplified using the primers described by Alvarado-Bremer34. Mitochondrial control region was also amplified for the identification of hake samples, but using primers MmerHK01 and MmerHK02, described by Lundy et al.36.

PCR amplifications were carried out using 10 pmol of each primer, 1.5–2.5 mM MgCl2, 0.25 mM dNTPs, 1 × Buffer GoTaq Promega, 0.15 µl of GoTaq Polymerase (5u/µL) and 2-4µL of DNA in a final volume of 20µL, with little differences among primers (Table 4).

PCR amplifications were carried out in a thermo cycler ABI PCR 2700 (Applied Biosystems) with the following conditions: an initial denaturing step of 90 °C for 5 min, followed by 35 cycles consisting of 30 s of denaturing at 90 °C, 30 s of annealing at 55 °C for hake-control region amplification and 57 °C for COI, an elongation step for 45 s at 72 °C, and a final elongation step at 72 °C for 15 min after the cycles for hake-control region, and for 10 min for COI. And an initial denaturing step of 94 °C for 5 min, followed by 35 cycles consisting of 45 s of denaturing at 94 °C, 45 s of annealing at 50 °C, an elongation step for 1 min at 72 °C, and a final elongation step at 72 °C for 5 min for anchovy and tuna control region. Resulting amplicons were sent for Sanger sequencing to Macrogen Inc. (Spain).

Sequences were edited with BioEdit v. and Lasergene Seqman software by DNASTAR37. Sequences obtained from forward and reverse primers separately were combined whenever possible to obtain a more robust barcode. Edited sequences were compared to GenBank sequence database, using BLAST (Basic Tool Alignment Search Tool) ( optimized for highly similar sequences (megablast), with word size = 28, match/mismatch scores of 1 and -2, linear gap costs, threshold 0.05. The best match hit was chosen as putative species of the sample from DNA.

Survey to investigate consumers’ preferences

To know the habits and preferences regarding fish consumption, a survey was conducted in urban and rural geographical areas of Asturias. This Spanish province of 1,000,000 inhabitants has a rich tradition of fishing and seafood is important in its gastronomy. In a face-to-face format, the researchers went to secondary education centers, university schools/faculties and consumer centers for its application. Participants were asked to indicate their age, gender and place of residence. Two questions about fish consumption followed. The first question (five possible choices) was: “How frequently do you consume fish normally? Please choose one of the following options: (1) More than 5 days a week; (2) Between 2 and 5 days a week; (3) One or two days a week; (4) Monthly; (5) Less than monthly”. Then they were asked for the frequency of consumption of each of the four target species using the following question: “How frequently do you eat the following species? Please choose the option that fits best your consumption habits”, with four possible choices: (1) Weekly; (2) Monthly; (3) Occasionally; (4) Rarely/Never”.


To test hypothesis a about fraudulent mislabeling, comparison between different types of samples (e.g. recognizable versus not recognizable, cheap versus expensive species, etc.) regarding the proportion of mislabeled samples was done employing the risk ratio (RR), with z tests and their p Ho being RR = 1.

Differences in consumption between the four fish species based on the number of informers consuming a species weekly, monthly, occasionally or [rarely + never] (four classes, four species) were tested using Chi-square of contingency with nine degrees of freedom.

The variable Consumer’s appreciation of a species was estimated based on the average of total fish consumption. The proportion of participants that consume a fish species (anchovy, hake, tuna, blue whiting) at a frequency at least equal to the average frequency of any fish consumption (first question) was taken as a proxy of regional consumer’s appreciation of that species. Pairwise correlations between this variable, price and proportion of mislabeled products were calculated using Pearson’s r. The hypothesis of price as a mediator between consumer’s appreciation and mislabeling was tested from the distribution of products following MacKinnon et al.38 and Wuensch39, taking into account the small number of fish species where substitution was measured. Briefly, β = unstandardized regression coefficient for predicting the mediator (price) from the independent variable (consumer’s appreciation), and α = partial unstandardized regression coefficient for predicting mislabeling from price holding constant the consumer’s appreciation were divided by the standard errors to calculate Zβ and Zα scores respectively. For a 0.05 non-directional test the critical value of the statistics Zα*Zβ is 2.18. Thus, higher values can be considered significant.

Significance threshold was p < 0.05. Statistics was done with free software PAST version 2.17c40.


  1. Helyar, S. J. et al. Fish product mislabelling: Failings of traceability in the production chain and implications for Illegal, Unreported and Unregulated (IUU) fishing. PLoS ONE 9(6), e98691. (2014).

    ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

  2. Delpiani, G. et al. Are we sure we eat what we buy? Fish mislabelling in Buenos Aires province, the largest sea food market in Argentina. Fish. Res. 221, 105373 (2020).

    Article  Google Scholar 

  3. Do, T. D. et al. Assessment of marine fish mislabeling in South Korea’s markets by DNA barcoding. Food Control 100, 53–57. (2019).

    CAS  Article  Google Scholar 

  4. Lamendin, R., Miller, K. & Ward, R. D. Labelling accuracy in Tasmanian seafood: An investigation using DNA barcoding. Food Control 47, 436–443. (2015).

    CAS  Article  Google Scholar 

  5. Nedunoori, A., Turanov, S. V. & Kartavtsev, Y. P. Fish product mislabeling identified in the Russian far east using DNA barcoding. Gene Rep. 8, 144–149. (2017).

    Article  Google Scholar 

  6. Mariani, S. et al. Low mislabeling rates indicate marked improvements in European seafood market operations. Front. Ecol. Environ. 13, 536–540 (2015).

    Article  Google Scholar 

  7. Blanco-Fernandez, C., Garcia-Vazquez, E. & Machado-Schiaffino, G. Seventeen years analysing mislabelling from DNA barcodes: towards hake sustainability. Food Control (2021).

    Article  Google Scholar 

  8. Barcaccia, G., Lucchin, M. & Cassandro, M. DNA barcoding as a molecular tool to track down mislabeling and food piracy. Diversity (2016).

    Article  Google Scholar 

  9. Pérez, M., Santafé-Muñoz, A. M., Balado, M. & Presa, P. Methodological evaluation of DNA-based molecular keys to identify categories of mislabelling in commercial products from genus Merluccius spp. Food Chem. 239, 640–648. (2018).

    CAS  Article  PubMed  Google Scholar 

  10. Crego-Prieto, V. et al. Inaccurate labelling detected at landings and markets. The case of European megrims. Fish. Res. 129–130, 106–109. (2010).

    Article  Google Scholar 

  11. Garcia-Vazquez, E., Machado-Schiaffino, G., Campo, D. & Juanes, F. Species misidentification in mixed hake fisheries may lead to overexploitation and population bottlenecks. Fish. Res. 114, 52–55 (2012).

    Article  Google Scholar 

  12. Galal-Khallaf, A., Ardura, A., Mohammed-Geba, K., Borrell, Y. J. & Garcia-Vazquez, E. DNA barcoding reveals a high level of mislabeling in Egyptian fish fillets. Food Control 46, 441–445 (2014).

    CAS  Article  Google Scholar 

  13. Muñoz-Colmenero, M., Blanco, O., Arias, V., Martinez, J. L. & Garcia-Vazquez, E. DNA authentication of fish products reveals mislabeling associated to seafood processing. Fisheries 41(3), 130–140 (2016).

    Article  Google Scholar 

  14. Muñoz-Colmenero, M., Juanes, F., Dopico, E., Martinez, J. L. & Garcia-Vazquez, E. Economy matters: A study of mislabeling in salmon products from two regions, Alaska and Canada (Northwest of America) and Asturias (Northwest of Spain). Fish. Res. 195, 180–185 (2017).

    Article  Google Scholar 

  15. Pardo, M. A. et al. DNA barcoding revealing mislabeling of seafood in European mass caterings. Food Control 92, 7–16 (2018).

    Article  Google Scholar 

  16. Kroetz, K. et al. Consequences of seafood mislabeling for marine populations and fisheries management. Proc. Natl. Acad. Sci. USA 117(48), 30318–30323 (2020).

    CAS  Article  Google Scholar 

  17. Pazartzi, T. et al. High levels of mislabeling in shark meat – Investigating patterns of species utilization with DNA barcoding in Greek retailers. Food Control 98, 179–186. (2019).

    CAS  Article  Google Scholar 

  18. Almerón-Souza, F. et al. Molecular identification of shark meat from local markets in Southern Brazil based on DNA barcoding: Evidence for mislabeling and trade of endangered species. Front. Genet. 9, 1–12. (2018).

    CAS  Article  Google Scholar 

  19. Miller, D., Jessel, A. & Mariani, S. Seafood mislabelling: Comparisons of two western European case studies assist in defining influencing factors, mechanisms and motives. Fish Fish. 13, 345–358. (2012).

    Article  Google Scholar 

  20. Jacquet, J. L. & Pauly, D. Trade secrets: Renaming and mislabeling of seafood. Mar. Policy 32(3), 309–318 (2008).

    Article  Google Scholar 

  21. Fox, M., Mitchell, M., Dean, M., Elliott, C. & Campbell, K. The seafood supply chain from a fraudulent perspective. Food Secur. 10, 939–963. (2018).

    Article  Google Scholar 

  22. Muñoz-Colmenero, M. et al. Evolution of hake mislabeling niches in commercial markets. Food Control 54, 267–274 (2015).

    Article  Google Scholar 

  23. Willette, D. A. et al. Using DNA barcoding to track seafood mislabeling in Los Angeles restaurants. Conserv. Biol. 31, 1076–1085. (2017).

    Article  PubMed  Google Scholar 

  24. Carvalho, D. C., Palhares, R. M., Drummond, M. G. & Gadanho, M. Food metagenomics: Next generation sequencing identifies species mixtures and mislabeling within highly processed cod products. Food Control 80, 183–186. (2017).

    CAS  Article  Google Scholar 

  25. Gordoa, A., Carreras, G., Sanz, N. & Viñas, J. Tuna species substitution in the Spanish commercial chain: a knock-on effect. PLoS ONE 12(1), e0170809. (2017).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  26. Sotelo, C. G. et al. Tuna labels matter in Europe: mislabelling rates in different tuna products. PLoS ONE 13(5), e0196641. (2018).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  27. IUCN (2020). The IUCN Red List of Threatened Species. Version 2020–3. Downloaded on January 2021.

  28. Rodriguez, N., Fernandez-Raigoso, M., Masia, P., Garcia-Vazquez, E. & Dopico, E. Seafood consumption in north Spain. Mendeley Data (2021).

    Article  Google Scholar 

  29. Jérôme, M. et al. Toward fish and seafood traceability: anchovy species determination in fish products by molecular markers and support through a public domain database. J. Agric. Food Chem. 56(10), 3460–3469 (2008).

    Article  Google Scholar 

  30. Velasco, A., Aldrey, A., Perez-Martin, R. & Sotelo, C. G. Assessment of the labeling accuracy of Spanish semipreserved anchovies products by FINS (forensically informative nucleotide sequencing). Heliyon 2, e00124. (2016).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  31. Fernández-Peralta, L., Quintanilla, L. F., & Rey, J. (2017). Overlapping distribution of two sympatric species: the case of black hakes, Merluccius polli Cadenat 1960 and Merluccius senegalensis Cadenat 1960, off Mauritania. In Deep-Sea Ecosystems Off Mauritania (pp. 241–275). Springer, Dordrecht.

  32. FAO (2020). Report of the Twenty-Second Session of the Fishery Committee for the Eastern Central Atlantic, Libreville, Gabon, 17–19 September 2019. FAO Fisheries and Aquaculture Report. No. 1303. Rome.

  33. ALLEA (2017). The European Code of Conduct for Research Integrity. Revised Edition. - All European Academies: Berlin.

  34. Froese, R. & Pauly, D. (eds) FishBase 2000: concepts, design and data sources 344 (ICLARM, 2000).

    Google Scholar 

  35. Ward, R. D., Zemlak, T. S., Innes, B. H., Last, P. R. & Hebert, P. D. N. DNA barcoding Australia’s fish species. Philosophical Trans. Royal Soc. B Biol. Sci. 360, 1847–1857. (2005).

    CAS  Article  Google Scholar 

  36. Alvarado Bremer, J.R. (1994). Assessment of morphological and genetic variation of the swordfish (Xiphias gladius Linnaeus): evolutionary patterns of nucleotide substitution in the mitochondrial genome. Toronto, Ontario, Canada: University of Toronto. Ph.D. dissertation.

  37. Viñas, J. & Tudela, S. A Validated Methodology for Genetic Identification of Tuna Species (Genus Thunnus). PLoS ONE 4(10), e7606 (2009).

    ADS  Article  Google Scholar 

  38. Lundy, C. J., Rico, C. & Hewitt, G. M. Temporal and spatial genetic variation in spawning grounds of European hake (Merluccius merluccius) in the Bay of Biscay. Mol. Ecol. 9, 2067–2079. (2000).

    CAS  Article  PubMed  Google Scholar 

  39. Burland T.G. (2000) DNASTAR’s Lasergene Sequence Analysis Software. In: Misener S., Krawetz S.A. (eds) Bioinformatics Methods and Protocols. Methods in Molecular Biology™, vol 132. Humana Press, Totowa, NJ.

  40. MacKinnon, D. P., Lockwood, C. M., Hoffman, J. M., West, S. G. & Sheets, V. A comparison of methods to test mediation and other intervening effects. Psychol. Methods 12, 23–44 (2002).

    Google Scholar 

  41. Wuensch, K.L. (2012). Screening data. Retrieved from on January 2021.

  42. Hammer, Ø., Harper, D. A. T. & Ryan, P. D. PAST: Paleontological statistics software package for education and data analysis. Palaeontol. Electron. 4(1), 9 (2001).

    Google Scholar 

Download references


We are grateful to Marta Rodríguez (Asturpesca SL) for help with sampling, and to the Environmental Health and Consumer Agency of the Principality of Asturias for their collaboration in the social survey. Ana Palacio, Celia Delgado, Pablo Alba and Sara Rodríguez de Diego contributed to laboratory tasks.


Funding was provided by Ministerio de Ciencia e Innovación grant number PID2019-108347RB-I00 and Gobierno del Principado de Asturias grant number GRUPIN IDI-2018-000201.

Author information

Authors and Affiliations



CBF and GMS led molecular analysis, AA coordinated sampling and laboratory work, ED coordinated the social survey, AR worked crucially on sampling, LV contributed to DNA analysis, NR and PM conducted the social survey where MFR contributed significantly, EGV designed the study and coordinated data analysis and manuscript writing, all the authors contributed to writing and manuscript preparation.

Corresponding author

Correspondence to Eva Garcia-Vazquez.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Publisher's note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Blanco-Fernandez, C., Ardura, A., Masiá, P. et al. Fraud in highly appreciated fish detected from DNA in Europe may undermine the Development Goal of sustainable fishing in Africa. Sci Rep 11, 11423 (2021).

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI:


By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.


Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing